Pulse oximetry method and system with improved motion correction

ABSTRACT

A pulse oximetry method and system for improved motion correction is disclosed. The method/system provides for the use of a detector output signal to obtain a different plurality of differential absorption data sets in corresponding relation to each of a succession of measurement, wherein each of the data sets includes differential absorption values for light of a first wavelength and light of a second wavelength. The data sets are processed to obtain a relative motion estimate value for each measurement. When the relative motion estimate value for a given measurement falls within a predetermined range (i.e., corresponding with clinical motion), a corresponding blood analyte indicator value is adjusted in a predetermined manner, wherein the corresponding adjusted blood analyte indicator is employable to obtain at least one blood analyte concentration value. In one embodiment, blood analyte indicator values may be readily multiplied by a predetermined adjustment factor (i.e., when clinical motion is identified). The relative motion estimate value for a given measurement Sep. 27, 2000 may be obtained by conducting a principal component analysis of the corresponding plurality of data sets relative to a corresponding best fit function therefor to obtain corresponding variance values V 1 , V 2 . The variance value V 1 , and/or V 2  for a given current measurement may be employed to obtain a current motion estimate value. The current motion estimate value and the relative motion estimate value obtained for a prior low motion measurement (i.e., for which no adjustment was necessary) may be used to compute the relative motion estimate value for the current measurement. The variance values V 1  and/or V 2  are also employable to compute an ongoing, updated motion probability factor, wherein such factor may be used to adjust relative motion estimates values in instances of rapid tissue perfusion changes.

FIELD OF THE INVENTION

The present invention generally pertains to patient monitoring usingphotoplethysmographic devices to generate blood analyte information.More particularly, the invention is directed to an efficient andeffective approach for reducing the undesired effects ofmotion-contaminated data in pulse oximetry devices.

BACKGROUND OF THE INVENTION

In the field of photoplethysmography light signals corresponding withtwo or more different centered wavelengths may be employed tonon-invasively determine various blood analyte concentrations. By way ofprimary example, blood oxygen saturation (SpO₂) levels of a patient'sarterial blood are monitored in pulse oximeters by measuring theabsorption of oxyhemoglobin and reduced hemoglobin using red andinfrared light signals. The measured absorption data allows for thecalculation of the relative concentrations of reduced hemoglobin andoxyhemoglobin, and therefore SpO₂ levels, since reduced hemoglobinabsorbs more light than oxyhemoglobin in the red band and oxyhemoglobinabsorbs more light than reduced hemoglobin in the infrared band, andsince the absorption relationship of the two analytes in the red andinfrared bands is known.

To obtain absorption data, pulse oximeters comprise a probe that isreleaseably attached to a patient's appendage (e.g., finger, ear lobe orthe nasal septum). The probe directs red and infrared light signalsthrough the appendage, or tissue-under-test. The light signals areprovided by one or more sources which are typically disposed in theprobe. A portion of the light signals is absorbed by thetissue-under-test and the intensity of the light transmitted through thetissue-under-test is detected, usually by at least one detector that maybe also located in the probe. The intensity of an output signal from thedetector(s) is utilized to compute SpO₂ levels, most typically via aprocessor located in a patient monitor interconnected to the probe.

As will be appreciated, pulse oximeters rely on the time-varyingabsorption of light by a tissue-under-test as it is supplied withpulsating arterial blood. The tissue-under-test may contain a number ofnon-pulsatile light absorbers, including capillary and venous blood, aswell as muscle, connective tissue and bone. Consequently, detectoroutput signals typically contain a large non-pulsatile, or DC,component, and a relatively small pulsatile, or AC, component. It is thesmall pulsatile, AC component that provides the time-varying absorptioninformation utilized to compute arterial SpO₂ levels.

In this regard, the red and infrared signal portions of pulse oximeterdetector output signals each comprise corresponding large DC andrelatively small AC components. The red and infrared signal portionshave an exponential relationship to their respective incidentintensities at the detector(s). As such, the argument of the red andinfrared signal portions have a linear relationship and such portionscan be filtered and processed to obtain a ratio of processed red andinfrared signal components (e.g., comprising their corresponding AC andDC components), from which the concentration of oxyhemoglobin andreduced hemoglobin in the arterial blood may be determined. See, e.g.,U.S. Pat. No. 5,934,277. By utilizing additional light signals atdifferent corresponding centered wavelengths it is also known thatcarboxyhemoglobin and methemoglobin concentrations can be determined.See, e.g., U.S. Pat. No. 5,842,979.

As noted, the pulsatile, AC component of a pulse oximeter detectoroutput signal is relatively small compared to the non-pulsatile DCcomponent. Consequently, the accuracy of analyte measurements can beseverely impacted by small amounts of noise. Of particular concern hereis noise that contaminates absorption data as a result of undesiredvariations in the path length of light signals as they pass through thetissue-under-test. Such variations are most typically caused by patientmovement of the appendage to which a pulse oximetry probe is attached.

A number of different approaches have been utilized to reduce thedeleterious effects of patient motion in pulse oximeters. For example,pulse oximeter probes have been developed to enhance the physicalinterface between the probe and tissue-under-test, including thedevelopment of various clamp type probe configurations and securewrap-type probe configurations. Further, numerous approaches have beendeveloped for addressing motion contaminated data through dataprocessing techniques. While such processing techniques have achieved adegree of success, they often entail extensive signal processingrequirements, thereby contributing to increased device complexity andcomponentry costs.

SUMMARY OF THE INVENTION

In view of the foregoing, a general objective of the present inventionis to provide an improved method and system for addressing motioncorrection in pulse oximeters.

More particularly, primary objectives of the present invention are toprovide for motion correction in pulse oximeters in a manner that iseffective and that reduces complexity and component requirementsrelative to data processing-intensive prior art devices.

The above objectives and additional advantages are realized in thepresent invention. In this regard, the present inventor has recognizedthat patient motion can be generally classified into a limited number ofmotion ranges, or bands, for motion correction purposes. A first type ofmotion, which may be referred to as “low motion”, corresponds with arange of patient motions that do not have a significant effect on theabsorption data comprising a detector output signal. A second type ofmotion, which may be referred to as “clinical motion”, corresponds withpatient motion that primarily affects the AC component of a detectoroutput signal in a relatively predictable manner across a range ofmotion severity. For such clinical motion, the present inventor hasfurther recognized that a predetermined adjustment factor may beeffectively utilized to correct blood analyte measurements, therebyavoiding complex data processing requirements.

The above-noted recognitions provide the basis for a number ofimprovements to pulse oximetry systems in which a detector provides anoutput signal indicative of light absorption of a tissue-under-test ateach of a plurality of different centered light wavelengths (e.g.centered at red and infrared wavelengths) and which utilize the outputsignal to obtain blood analyte indicator values for each of a successionof measurements (e.g., periodic measurements corresponding withpartially overlapping or non-overlapping measurement data) during apatient monitoring procedure. The inventive method/system provides forthe utilization of the detector output signal to obtain a correspondingrelative motion estimate value for each measurement. For such purposes,the detector output signal may be processed to yield a differentplurality of differential absorption data sets for each measurement,wherein each data set includes a first differential absorption value forlight at the first wavelength dA_(λ) ₁ (e.g., infrared light) and acorresponding-in-time second differential absorption value for light atthe second wavelength dA_(λ) ₂ (e.g., infrared light). As will beappreciated, the plurality of data sets corresponding with each givenmeasurement are obtained over an associated time period, wherein eachsuccessive measurement employs a different successive plurality of datasets, and wherein the data sets employed for successive measurements mayor may not partially overlap. By way of primary example, thedifferential absorption data sets may be obtained via derivative orlogarithmic processing of a series of data samples that correspond witha detected infrared portion of a detector output signal and acorresponding-in-time series of data samples that correspond with adetected red light portion of the detector output signal.

For each of the measurements the method/system provides for adetermination of whether or not the corresponding relative motionestimate value (RMEV) is within a first predetermined range (e.g.corresponding with a predetermined range of clinical motion), whereinfor measurements having a corresponding RMEV within the firstpredetermined range the corresponding blood analyte indicator value(BAIV) may be adjusted in a predetermined manner. Such adjustment mayprovide for the use of a predetermined adjustment factor (e.g.,determined empirically). For measurements indicating clinical motion,the adjusted BAIVs may be utilized to obtain a more accurate measure ofblood analyte concentration (e.g. the relative concentration ofoxygenated hemoglobin and reduced hemoglobin for SpO₂ leveldetermination). As will be appreciated, one or more clinical motionbands, with corresponding predetermined RMEV ranges and adjustmentfactors, may be employed in accordance with the present invention.

As noted, the present inventor has recognized that for a certain rangeof patient motion, referred to as low motion herein, the impact of themotion on blood analyte measurements is negligible. As such, theinventive method/system may further provide for a determination as towhether the relative motion estimate value (RMEV) for each of theplurality of measurements is within a second predetermined range (e.g.,corresponding with low motion). For each measurement having acorresponding RMEV within the second predetermined range, thecorresponding blood analyte indicator value may be employed withoutadjustment to obtain blood analyte concentration values.

The RMEV obtained with respect to each given measurement period mayentail the computation of a best-fit function for the correspondingplurality of differential absorption data sets. By way of example, thebest-fit function may be defined as the slope value of a regression line(e.g. determined via least squares or linear regression processing) fora “plot” of the differential absorption values for light at the firstwavelength dA_(λ) ₁ versus the corresponding-in-time differentialabsorption values for light at the second wavelength dA_(λ) ₂ . As maybe appreciated, the best fit function obtained with respect to eachmeasurement period may also be utilized as the corresponding BAIV, e.g.,the “best-fit RRatio” value as taught in U.S. Pat. No. 5,954,277.Alternatively, a first plurality of differential absorption data setsmay be computed for use in the computation of the RMEV; and a secondplurality of differential absorption data sets may be separatelycomputed for use in the determination of the BAIVs as taught in aco-pending U.S. patent application entitled “Method And Apparatus ForDetermining Pulse Oximetry Differential Values”, filed contemporaneousherewith and hereby incorporated by reference.

In conjunction with the obtainment of a best fit function for each givenmeasurement, the inventive method/system may further provide for theperformance of a statistical analysis of the corresponding plurality ofa differential absorption data sets in relation to the best fit functionto obtain at least one statistical variance value indicative of a degreeof any associated motion, wherein the statistical variance value(s) maybe utilized to compute the corresponding RMEV. In primary embodiments,the statistical analysis may comprise a principal component analysis(PCA). That is, for each measurement the plurality of differentialabsorption data sets (i.e., each comprising corresponding-in-time dA_(λ)₁ and dA_(λ) ₂ values) may be statistically analyzed in relation to thecorresponding best fit function to obtain at least one of a firstprincipal component variance value (V₁) and a second principal componentvariance value (V₂). In this regard, V₁ may be computed to represent anamplitude-based statistical variance, while V₂ may be computed torepresent a scatter-based statistical variance. The relative motionestimate value for a given measurement may be determined utilizing atleast one, and preferably both, of the corresponding first and secondprincipal component variance values V₁ and/or V₂.

More particularly, in one embodiment, for each given one of a pluralityof measurements the inventive method/system may provide for thecalculation of a corresponding current motion estimate value (CMEV)utilizing the corresponding V₁ and V₂ values, and the identification ofa reference motion value (RMV), wherein the corresponding relativemotion estimate value (RMEV) is computed utilizing both the CMEV andRMV. In one arrangement, the CMEV for each given measurement may becalculated as follows:

CMEV=V ₁ *V ₂

In turn, the RMEV for each given measurement may be calculated as aratio of the corresponding CMEV and RMV, e.g.:${RMEV} = {\frac{CMEV}{RMV}.}$

The RMV may be established on an ongoing, updated basis to be equal tothe RMEV that corresponds with the lowest amount of motion (e.g., thelowest RMEV) determined with respect to any prior measurement (e.g.preferably corresponding low motion) for a given patient monitoringprocedure (i.e., a “low motion reference”). At the outset of a givenmeasurement procedure and/or where there is otherwise no prior lowmotion reference basis for establishing an RMV, the RMV for the givenmeasurement may be established as follows:${{RMV} = {\frac{V_{1}}{V_{2}}\quad {CMEV}^{*}K}},$

where V₁, V₂ and CMEV are as determined with respect to the givencurrent measurement and K is a constant.

As indicated, for measurements having an RMEV within a firstpredetermined range, i.e., corresponding with clinical motion, apredetermined adjustment factor (PAF) may be utilized to adjust thecorresponding blood analyte indicator value (BAIV) for use in bloodanalyte concentration computations. Such adjustment may entail the readyapplication, e.g., by multiplication, of the PAF to the computed BAIV.In this regard, the PAF may be empirically set via statistical analysisof clinical motion-affected absorption data and corresponding-in-time,non-motion-affected absorption data obtained via testing of test subjectcontrol groups.

In one embodiment, for measurements having computed BAIVs that exceed apredetermined threshold value, the PAF may be scaled in relation to thepredetermined threshold value. For example, in applications where eachBAIV is defined by the slope value of a regression line, the PAF for agiven BAIV may be scaled follows:${{{Scaled}\quad {PAF}} = {1 - \left\lbrack {\left\lbrack \frac{{BAIV} - A}{B - A} \right\rbrack*\left( {1 - {PAF}} \right)} \right\rbrack}},$

wherein A and B are predetermined constants, and wherein no scalingoccurs when:

BAIV>B,

and wherein PAF may be set to 1 when:

BAIV<A.

In arrangements where the BAIVs are defined by regression line slopevalues as noted above, the PAF may be preferably set between about 0.5and 0.85, and most preferably between about 0.6 and 0.75 (e.g., about0.6875 in one arrangement where A=0.9455 and B=0.65).

In additional aspects of the present invention, the inventivemethod/system may further comprise additional features to insureappropriate motion correction where there has been a rapid, significantchange in the perfusion of a tissue-under-test. By way of example, suchvariations may occur as a result of the application of a tourniquet orblood pressure cuff, or as a result of inadvertent contact with apatient “pressure point” (e.g., that cuts off arterial blood flow to thetissue under test). To address such instances the inventivemethod/system may provide for the ongoing, periodic determination of amotion probability factor (MPF), wherein if the MPF exceeds apredetermined threshold the reference motion value (RMV) may be adjustedusing the MPF.

The MPF may be computed via a statistical analysis of the plurality ofdifferential absorption data sets corresponding with each of all givenone of a series of measurements in relation to a corresponding best fitfunction computed therefore, wherein at least one statistical variancevalue may be obtained for use as an MPF value. More particular, the MPFmay be computed utilizing of V₂ and/or V₁ values computed with respectto each current a series of measurements and computed with respect toone or more prior measurements. In one approach, an average V₂ value isdetermined in relation to each given measurement, wherein each averageV₂ value is calculated by averaging the sum of the V₂ value for a givenperiod and the V₂ values for a predetermined number of immediatelyprecedent measurements. Then, an MPF for each given measurement may becomputed utilizing a comparison, or ratio, between the average V₂ valuecomputed for the current measurement and the lowest average V₂ valuecomputed with respect to any prior measurement (e.g., current average V₂value/lowest average V₂ value).

In one arrangement, the system/method may be established so that an RMVadjustment may be made when (i) a predetermined number of successive,non-low motion measurements have occurred, and (ii) the motionprobability factor (MPF) for the current measurement period isdetermined to exceed a predetermined threshold. When the predeterminedthreshold is exceeded, the RMV may be adjusted as follows:

adjusted RMV=CMEV+(CMEV−RMV)(MPF)K,

wherein CMEV and MPF are as determined with respect to the currentmeasurement and K is a constant.

In addition to the implementation of one or more clinical motion rangesand a low motion range, embodiments of the present invention may alsoaddress patient motion that exceeds clinical motion and may be referredto as “severe motion”. In such embodiments, the present inventionaccommodates an approach wherein no blood analyte measurement values areoutput to a user during “severe motion” (e.g., when measurements havecorresponding RMEVs outside of the predetermined clinical motion and lowmotion RMEV ranges).

Alternatively, to address severe motion embodiments may be employedwhich provide for the use and adjustment of a previously determinedblood analyte indicator value (BAIV). For example, the BAIV for the mostrecent non-severe motion measurement may be adjusted by an adjustmentfactor for use in blood analyte concentration computations. Theadjustment factor may be based upon tracking of the DC components of thedetector output signal portion(s) corresponding with the detected lightat the first and/or second centered-wavelength(s) signals. That is, theadjustment factor may be computed as the ratio between such DCcomponent(s) measurement, and such DC components corresponding with atleast a current, given severe motion measurement (i.e. a measurementobtained utilizing data obtained during severe motion.

In this regard, in one embodiment the detector output signal may beutilized to obtain moving average values of total tissue absorption ofthe AC and DC signal components corresponding with each of the first andsecond light signals (MAV_(λ1), MAV_(λ2)), e.g., for red and infraredsignal components, wherein such moving average values are computed for apredetermined precedent time interval in conjunction with eachmeasurement (e.g., an average for a predetermined number of measurementsthat include the current measurement and a number of priormeasurements). In the event severe motion is detected with respect to agiven measurement period, e.g. when the corresponding RMEV is not withina predetermined low motion range or any predetermined clinical motionrange, the BAIV corresponding with the most recent, non-severe motionmeasurement may be adjusted by a DC tracking factor (DCTF), as follows:${{DCTF} = {\frac{\frac{{MAV}_{\lambda \quad 2}}{{MAV}_{\lambda \quad 1}}\quad {for}\quad {most}\quad {recent}\quad {pre}\text{-}{severe}\quad {motion}\quad {measurement}}{\frac{{MAV}_{\lambda \quad 2}}{{MAV}_{\lambda \quad 1}}\quad {for}\quad {current}\quad {severe}\text{-}{motion}\quad {measurement}}\quad K}},$

wherein K is a constant; then:

adjusted BAIV=BAIV*DCTF.

The adjusted BAIV may then be employed for enhanced blood analytedeterminations for the given severe motion measurement. As may beappreciated, this inventive aspect may be advantageously utilized in anumber of arrangements, including embodiments where no clinical motionbands are defined/employed for motion correction purposes. For example,this feature may be separately utilized with the system/method disclosedin PCT Publication No. WO 98/04903, PCT Application No. PCT/CH97/00282,hereby incorporated by reference.

Additional aspects and other combinations of the present invention willbe apparent to those skilled in the art upon review of the furtherdescription that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one embodiment of a system forimplementation of the present invention.

FIG. 2 illustrates an exemplary pulse waveform received on channel X ofthe FIG. 1 embodiment.

FIG. 3 illustrates an exemplary pulse waveform received on channel Y ofthe FIG. 1 embodiment.

FIG. 4 is plot of differential absorption data set values dA_(x), dA_(y)obtained via channels X and Y of the FIG. 1 embodiment, andcorresponding with low patient motion.

FIG. 5 is a plot illustrating exemplary 45-degree bias lines for datasets affected by motion.

FIG. 6 is a plot illustrating first and second principal components in aprincipal component analysis (PCA) of a set of differential absorptionvalues.

FIG. 7 is a flow diagram of one embodiment of methodology comprising thepresent invention.

FIG. 8 is a flow diagram of one approach for obtaining relative motionestimate values (RMEVs) in the embodiment of FIG. 7.

FIG. 9 is a flow diagram of one approach for correcting blood analyteindicator values (BAIVs) in the embodiment of FIG. 7.

DETAILED DESCRIPTION

Referring to FIG. 1, a pulse oximeter system 100 according to oneembodiment of the present invention is shown. Included in the system 100are two light emitters 102 for radiating red light signals and infraredlight signals and a photodetectors 104. By way of example, the lightemitters 102 may be light emitting diodes (LEDs) or laser diodes.

The emitters 102 and photodetector(s) 104 may be incorporated into aprobe 106 adapted for removable attachment to an appendage 100 of apatient, such as a finger, earlobe, nasal septum, or other tissue,during a monitoring procedure. In use, the probe 106 directs the lightsignals generated by the light emitters 102 onto one side of theappendage 100. Photodetector(s) 104 is positioned on the opposite sideof the appendage 108 to monitor the intensity of light that istransmitted through the tissue and produce an output signal responsivethereto. As may be appreciated, the red and infrared emitters 102 may bedriven by a drive 110 within interconnected processor unit 120 so thatthe output signal of detector(s) 104 is multiplexed according to apredetermined scheme (e.g., time-division multiplexed, frequencydivision multiplexed, code-division multiplexed, etc.).

In turn, a demultiplexer 112 may be provided to separate the detectoroutput signal into infrared and red signal portions on two processingchannels, e.g. channels X and Y, respectfully. Signal converters 114 maybe provided in each channel X, Y, with current-to-voltage amplifiers,analog-to-digital converters, and other componentry for digitizing thered and infrared signal portions for processing by processor unit 120.As will be appreciated, demultiplexer 112 may be provided in a hardwareform or implemented in software downstream of converters 114 atprocessor unit 120.

In accordance with the described system embodiment 100, the processorunit 120 is provided for automatic motion correction in an effective andefficient manner. For such purposes, processor unit 120 may comprise afront-end module 136 that receives digitized AC and DC detector signalvalues on both channels X and Y and generates a series of compositeinfrared and composite red signal values, respectively (e.g., 30 valueseach per sec.). Such processing may entail the use of preset AC gain andDC gain values, preset A/D ground values, and preset light emitter drivelevels to normalize the AC and DC values. Processor unit 120 may furthercomprise one or more data storage buffer(s) 122 to temporarily storedata sets comprising the composite red and infrared signal valuesreceived from module 136, as well as additional values computed bycomputation modules of processor unit 120. In the later regard, the datastored in buffer(s) 122 may be accessed for use by a number ofpreprogrammed computation modules comprising the processor unit 120.

In particular, a blood analyte indicator value computation module 124may access the composite infrared and red data signal values inbuffer(s) 122 to compute differential infrared and red absorption datasets (e.g., 30 sets/sec.) from which blood analyte indicator values maybe determined for each of a succession of measurements (e.g., every ½sec.) during patient monitoring. Each blood analyte indicator value(BAIV) may be defined to corresponded with a best fit functiondetermined for a plurality of data sets (e.g., 64 sets) utilized for agiven measurement. Computed BAIVs may be at least temporarily stored atbuffer(s) 122 for use in accordance with the described embodiment.

A relative motion estimate value computation module 126 may also accessthe composite infrared and red data signal values in buffer(s) 122 tocompute differential absorption data sets (e.g., 30 sets sec.) fromwhich relative motion estimate values may be successively determined foreach measurement (e.g., every ½ sec.) during patient monitoring. In thisregard, module 126 may utilize a plurality of differential absorptiondata sets (e.g., 64 sets) for a given measurement to determine a bestfit function and corresponding motion variance valves V₁, V₂ via aprincipal component analysis (PCA). The motion variance values V₁, V₂corresponding with each given measurement period provide an indicationof the degree of patient motion occurring during the period associatedwith data sets employed for such measurement. Module 126 may utilize theV₁ and/or V₂ value(s) to compute a current motion estimate value (CMEV)for each measurement, and to establish a reference motion value (RMV).The CMEV and RMV may be employed in module 126 to compute a referencemotion estimate value (RMEV) for each measurement. Computed RMEV and RMVvalues may be at least temporarily stored at buffer(s) 122.

As indicated by FIG. 1, the blood analyte indicator values computed atmodule 124 and relative motion estimate values computed at module 126may be provided to an adjustment module 128 that may adjust, or correct,the blood analyte indicator value (BAIV) for each given measurement whenthe corresponding relative motion estimate value (RMEV) is within afirst predetermined motion range corresponding with clinical motion.When the RMEV corresponding with a given measurement period falls withina second predetermined range corresponding with low patient motion(e.g., non-overlapping with the first range), the BAIV for thatmeasurement will not be adjusted by module 128. As will be appreciated,the adjusted and unadjusted BAIVs corresponding with successivemeasurements may be provided to an analyte computation module 130 forcomputation of blood analyte concentrations. The computed concentrationlevels may be output to user, e.g., via a display 140 provided at apatient monitor. The monitor may also house the emitter drive 110,signal converters 114, processor unit 120, and in alternate embodimentsdemultiplexer 112, emitters 102 and/or detector(s) 104.

Referring further to FIG. 1, processor unit 120 may also include amotion probability factor computation module 134 that may interface withmodule 126 and buffer(s) 122 to obtain computed RMEV and PCA variancevalues V₁ and/or V₂. Module 134 may employ such values to compute amotion probability factor (MPF) on an ongoing updated basis (e.g.corresponding with each measurement). In turn, the MPF may be utilizedto adjust the RMV utilized in module 126 to compute the RMEVs.

Finally, the processor 120 may further include an average absorptioncomputation module 132 which may access the composite infrared and redvalues in buffer(s) 122 to compute average total infrared and redabsorption values for predetermined intervals(e.g., corresponding with apredetermined number of successive measurements), in correspondingrelation to each given measurement. As will be further described, suchaverage absorption values may be utilized in certain embodiments inconnection with the correction of blood analyte indicator valuesutilized for measurements having corresponding relative motion estimatevalues that fall within a severe patient motion range.

As noted, modules 124 and/or 126 may access data buffer(s) 122 to obtaincomposite infrared and red values from which differential absorptiondata sets may be computed with respect to each given measurement. Forpurposes of describing such differential absorption data sets, referenceis now made to FIGS. 3 and 4 which illustrate pulse waveforms 300 and400, corresponding with the intensity of the digitized,corresponding-in-time composite infrared and red signal values generatedat module 136 of processor unit 120 for channels X and Y, respectively.By way of example, derivative processing of successive data samples onchannel X and on channel Y may be approximated to obtain differentialabsorption valves dA_(x) and dA_(y), as follows:

dA _(x) ≅DA _(x)=(I _(t,x) −I _(t−1,x))/[(I _(t,x) +I _(t−1,x))/2], and

 dA _(y) ≅DA _(y)=(I _(t,y) −I _(t−1,y))/[(I _(t,y) +I _(t−1,y))/2].

Alternatively, the differential absorption values may be obtained bycalculating differences between the logarithm values of successiveinfrared and successive red composite signal values.

In one embodiment, the computation of differential absorption valvesdA_(x), dA_(y) at modules 124 and 126 may be conducted separately,wherein module 126 employs processing techniques taught, in a copendingU.S. Patent Application entitled “Method and Apparatus for DeterminingPulse Oximetry Differential Values”, filed contemporaneous herewith andhereby incorporated by reference. Alternatively, modules 124 and 126 mayutilize the same dA_(x), dA_(y) values, with best fit functions computedfor each measurement utilized both modules 124 and 126.

To further describe such best fit functions, reference is now made toFIG. 5. In FIG. 5, a plurality of differential absorption data setsdA_(x), dA_(y) that correspond with low motion are presented. That is,each data point represents a plot of a differential absorption valuedA_(x) versus a corresponding-in-time differential absorption valuedA_(y). A linear regression analysis has been performed on the data setto determine a regression line 500, or best fit function, that best fitsthe data sets. The slope value of line 500 represents a normalized ratioof dA_(x), and dA_(y) for the corresponding measurement and may be usedin a known manner to determine an SpO₂ level.

As noted, module 126 of processor unit 120 computes a relative motionestimate value (RMEV) for each measurement. Each such value may bedetermined utilizing a computed best fit function for the correspondingdifferential absorption data sets. For purposes of further explanation,reference is now made to FIG. 6 which illustrates in plot form animportant observation of how motion may affect the data correspondingwith a given measurement period. In particular, it has been found thatfor motion within a clinical motion range the effects upon correspondingdifferential absorption values dA_(x) and dA_(y) may be substantiallythe same. Thus a plotted data point affected by motion may be biased,e.g., along a 45-degree angle line. The regression line 600 shows whereplotted data points should lie if no motion is present. The linesslanted at 45-degree 604 represent how data points 602 may be displacedwhen the differential absorption values dA_(x), and dA_(y) are affectedby clinical motion.

In one embodiment, the operation of module 126 is based upon theobservation described above in relation to FIG. 6. More particularly,module 126 may utilize a best fit function, or regression line, for eachgiven measurement to perform a principal component analysis (PCA) of thecorresponding differential absorption data sets, wherein variance valuesV₁ and V₂ may be determined. FIG. 7 graphically illustrates the resultsof a PCA performed on a plurality of data sets, having a best fit line700, to obtain corresponding V₁ and V₂ values. The distribution box 702of the data points (not shown individually) may be described using firstand second principal component vectors and derived from the PCA. Each ofthe principal component vectors accounts for a different amount ofvariation among the data points. The first principal component vectordescribes the cluster of data points disposed along a longitudinal axis704. The second principal component vector corresponds with an axis 706extending perpendicular to the first axis 704. With vectors describingvariations among the set of data points relative to axes 704, 706, anynumber of algorithms may be used to estimate an amount of motionassociated therewith.

For example, in one approach, let x denote the dA for channel X and ydenote the dA for channel Y. The calculations may be based on a bufferof n pairs of data (x_(i),y_(i)). The calculations may be based on thefive following summations:${S_{1} = {\sum\limits_{i = 1}^{n}\quad x_{i}}},{S_{2} = {\sum\limits_{i = 1}^{n}\quad x_{i}^{2}}},{S_{3} = {\sum\limits_{i = 1}^{n}\quad y_{i}}},{S_{4} = {\sum\limits_{i = 1}^{n}\quad y_{i}^{2}}},{and}$$S_{5} = {\sum\limits_{i = 1}^{n}\quad {x_{i}\quad {y_{i}.}}}$

These are then used to calculate

${u = {S_{2} - \frac{S_{1}^{2}}{n}}},$

${w = {S_{4} - \frac{S_{3}^{2}}{n}}},$

and

$p = {S_{5} - {\frac{S_{1}\quad S_{3}}{n}.}}$

The regression slope may then calculated to be$\beta = {\frac{w - u + \sqrt{\left( {u - w} \right)^{2} + {4 \cdot p^{2}}}}{2 \cdot p}.}$

The correlation coefficient between x and y is$r = {\frac{p}{\sqrt{u \cdot w}}.}$

The variation of the points along the regression line is the variance V₁associated with the first principal component, and the variation of theperpendicular distance to this line is the variance V₂ associated withthe second principal component. As previously noted, these values areuseful in evaluating the presence and degree of motion.

The variance V₁ of the first principal component may be expressed as:${V_{1} = \frac{u + {\beta^{2}\quad w} + {2\quad \beta \quad p}}{1 + \beta^{2}}},$

and the variance V₂ of the second principal component may be expressedas:$V_{2} = {\frac{w + {\beta^{2}\quad u} - {2\quad \beta \quad p}}{1 + \beta^{2}}.}$

The utilization of V₁ and V₂ values in conjunction with various modulescomprising processor 120 will be further described below.

In this regard, FIGS. 7-9 illustrate process steps in the operation ofone implementation of the present invention. As shown in FIG. 7, adetector output current signal may be initially converted (step 202)into a digital form and composite red and infrared values may beobtained from the converted signal. Following signal conversion, the redand infrared detector output signal portions may be processed to obtainat least a first plurality of differential absorption data sets for eachsuccessive measurement (step 204). A plurality of the data sets may beemployed to obtain a corresponding blood analyte indicator value (BAIV)for each measurement (step 206), e.g., a best fit function as describedabove. Correspondingly, a plurality of data sets may be utilized toobtain a relative motion estimate value (RMEV) for each measurement(step 208).

In one embodiment, the RMEV for each measurement may be determinedaccording to the method steps shown in FIG. 8. As illustrated therein, aprincipal component analysis (PCA) may be conducted on the data setscorresponding with each current measurement relative to a correspondingcomputed best fit function to obtain variance values V₁, V₂ (step 220).In turn, a current motion estimate value CMEV may be determined withrespect to each current measurement (step 222), as follows:

CMEV=V ₁ *V ₂.

As further shown in FIG. 8, a reference motion value (RMV) may also beestablished on an ongoing updated basis. More particularly, the RMV maybe established to be equal to the lowest RMEV for any prior measurementhaving an RMEV in the low motion range (steps 224, 226). If no prior lowmotion measurement is available (step 224), the RMV for a givenmeasurement may be determined as follows (step 228):

${{RMV} = {\frac{V_{1}}{V_{2}}\quad {CMEV}\quad K}},$

where V₁, V₂ and CMEV are determined in the manner described above forthe current measurement, and K is predetermined constant.

Pursuant to the determination of V₁ and V₂ values, the illustratedprocess may also provide for the computation of a motion probabilityfactor (MPF) on an ongoing, updated basis utilizing the V₂ measurementvalues (step 230). More particularly, an average V₂ value may becomputed for each measurement by averaging the V₂ value for the givenmeasurement together with the V₂ determined with respect topredetermined number of immediately precedent measurements. In turn, theMPF at a given measurement may be determined by comparing the average V₂value for the current measurement with the lowest average V₂ valuecomputed with respect to any prior measurement (e.g., by computing aratio value therebetween).

The MPF may be advantageously utilized to adjust the RMV (e.g., asdetermined in step 226) to address rapid changes in the perfusion of thetissue-under-test. In the illustrated process embodiment, suchadjustment may be made in the event that the MPF exceeds a predeterminedthreshold after a predetermined number of successive non-low motionmeasurements (step 232). In the event an adjustment to an RMV is to bemade, the MPF and RMV may be utilized together with the CMEV for thecurrent measurement to make such adjustment (step 234). Moreparticularly, such adjustment may be made as follows:

Δ Adjustment=(CMEV−RMV)*MPF,

and,

if iΔ Adjustment<CMEV*A,

then:

Δ Adjustment=CMEV*A,

and,

RMV=CMEV−Δ Adjustment,

wherein A is a predetermined constant.

As illustrated in FIG. 8, the CMEVs for each of the measurements,together with 15 the RMV (as set in Step 226, Step 228, or as adjustedin Step 234) may be utilized to compute the RMV for each measurement(step 236). In the illustrated embodiment, the RMV values may bedetermined as follows: ${{RMEV} = \frac{CMEV}{RMV}},$

wherein CMEV corresponds with the current measurement.

Returning now to FIG. 7, the illustrated process further provides for adetermination of whether or not the RMEV for each given measurement iswithin a predetermined low motion range (step 210). As previouslydescribed, the predetermined low motion range may be set so that noadjustment of the blood analyte indicator value computed at step 206 isnecessary (step 212). In the event that the RMEV for a given measurementdoes not fall within the low motion range, the corresponding bloodanalyte indicator may be corrected (step 214). For such purposes,reference will now be made to FIG. 9, which illustrates one embodimentfor correction.

In particular, and as shown in FIG. 9, an initial determination may bemade as to whether the relative motion estimate value (RMEV) for a givenmeasurement is within a predetermined clinical motion range (step 240).In the event clinical motion is identified, the corresponding bloodanalyte indicator value (BAIV) may be adjusted in a predeterminedmanner. In one embodiment a best fit function, or slope value, computedfor the given measurement may be simply adjusted by a predeterminedfactor to obtain an adjusted BAIV (step 242). By way of example, in oneembodiment if a given BAIV is greater than a predetermined constant Bthe BAIV may be multiplied by a predetermined adjustment factor (PAF) toobtain the adjusted BAIV. On the other hand, if the BAIV is less thanthe predetermined constant B yet greater than a predetermined constantA, the PAF may be scaled as follows:${{Scaled}\quad {AF}} = {1 - {\left( \frac{{BAIV} - A}{B - A} \right)*{\left( {1 - {PAF}} \right).}}}$

In the event that the BAIV for a given measurement is less than thepredetermined value A no adjustment of the BAIV is conducted.

In the event that a given relative motion estimate value (RMEV) is notwithin the second predetermined range, a number of options are possible.In one embodiment, a corrected blood analyte indicator may be obtainedutilizing a BAIV for a prior low motion measurement (step 244), asadjusted by DC tracking component (step 244). More particularly, theBAIV computed for the most recent non-severe motion measurementpreceding a given severe motion period may be utilized in thecomputation of a BAIV. In particular, the pre-severe motion measurementBAIV may be multiplied by a DC tracking factor (DCTF) to obtain a BAIVfor the severe motion measurement. The DCTF may be computed as follows:${{DCTF} = {\frac{\frac{{MAV}_{\lambda \quad 2}}{{MAV}_{\lambda \quad 1}}\quad {for}\quad {most}\quad {recent}\quad {pre}\text{-}{severe}\quad {motion}\quad {measurement}}{\frac{{MAV}_{\lambda \quad 2}}{{MAV}_{\lambda \quad 1}}\quad {for}\quad {current}\quad {measurement}}\quad K}},$

Wherein MAV_(λ1), MAV_(λ2) values are average total absorption valuesfor the infrared and red portions of the detector output signal,respectively, as computed for the pre-severe motion measurement and forthe current severe motion measurement, and K is a predeterminedconstant. To compute MAV_(λ1) MAV_(λ2) the light absorption values(i.e., DC plus AC components) for the infrared and red portions,respectively, of the detector output signal may be averaged for apredetermined number of successive measurements.

Returning now to FIG. 7, the illustrated embodiment finally provides forthe use of blood analyte indicators for each of the measurement periods,as either adjusted or unadjusted, to compute a blood analyteconcentration (step 212 and 216). By way of example, SpO₂ levels may becomputed in a manner as taught in U.S. Pat. No. 5,934,277. In turn, thecomputed SpO₂ values may be output to a display 140 as previously noted(step 218).

The embodiment described above is for exemplary purposes only and is notintended to limit the scope of the present invention. Variousadaptations, modifications and extensions of the described system/methodwill be apparent to those skilled in the art and are intended to bewithin the scope of the invention as defined by the claims which follow.

What is claimed is:
 1. A method for use in a pulse oximetry system whichprovides a detector output indicative of light absorption by atissue-under-test at each of a plurality of different light wavelengths,comprising: utilizing said detector output to (i.) compute blood analyteindicator values for each of a plurality of measurements, and (ii.)obtain a corresponding relative motion estimate value for each of saidplurality of measurements; and, determining whether the correspondingrelative motion estimate value for each of said plurality ofmeasurements is within a first predetermined range, wherein for at leastone of said plurality of measurements having a corresponding relativemotion estimate value within the first predetermined range thecorresponding blood analyte indicator value is adjusted utilizing apredetermined adjustment factor that is empirically determined, andwherein said adjusted blood analyte indicator value is employable toobtain a blood analyte concentration value.
 2. A method as recited inclaim 1, wherein for said at least one of said plurality of measurementshaving a corresponding relative motion estimate value within the firstpredetermined range said predetermined adjustment manner includes:adjusting the corresponding blood analyte indicator value utilizing apredetermined adjustment factor.
 3. A method as recited in claim 2,wherein said adjusting step includes: multiplying the correspondingblood analyte indicator value by said predetermined adjustment factor.4. A method as recited in claim 3, wherein said adjusting step furtherincludes: comparing the corresponding blood analyte indicator value to apredetermined threshold value, wherein said predetermined adjustmentfactor is scaled when said blood analyte indicator value exceeds saidpredetermined threshold value.
 5. A method as recited in claim 3,wherein said predetermined adjustment factor is set to a value betweenabout 0.5 and 0.85.
 6. A method as recited in claim 1, said utilizingstep including: processing said detector output to obtain a differentplurality of data sets corresponding with each of said plurality ofmeasurements, each of said plurality of data sets comprising a firstdifferential absorption value for light at a first wavelength and asecond differential absorption value for light at a second wavelength,wherein for each of said plurality of measurements a corresponding bestfit function is computed for the corresponding plurality of data sets.7. A method as cited in claim 6, wherein for each of said plurality ofmeasurements said utilizing step further includes: performing astatistical analysis of the corresponding plurality of data sets inrelation to the corresponding best fit function to obtain at least onestatistical variance value indicative of a relative degree of patientmotion; and, computing said corresponding relative motion estimate valueusing said corresponding at least one statistical variance value.
 8. Amethod as cited in claim 6, wherein for each of said plurality ofmeasurements said utilizing step further includes: performing aprincipal component analysis of the corresponding plurality of data setsin relation to the corresponding best fit function to obtain at leastone of a first principal component variance value (V₁) and a secondprincipal component variance value (V₂); and computing saidcorresponding relative motion estimate value using at least one of saidcorresponding first principal component variance value V₁ and saidcorresponding second principal component value V₂.
 9. A method asrecited in claim 8, wherein for each given one of said plurality ofmeasurements said utilizing step further includes: calculating acorresponding current motion estimate value (CMEV) using at least one ofsaid corresponding V₁ and V₂ values; establishing a reference motionvalue (RMV); and computing said corresponding relative motion estimatevalue (RMEV) using said corresponding CMEV and said RMV.
 10. A method asrecited in claim 9, wherein for each of said plurality of measurements:said CMEV is calculated as follows: CMEV=V ₁ *V ₂; and, saidcorresponding RMEV is determined as follows:${RMEV} = {\frac{CMEV}{RMEV}.}$


11. A method as recited in claim 10, wherein for at least one of saidplurality of measurements said establishing step includes:${{RMV} = {\frac{V_{1}}{V_{2}}\quad {CMEV}^{*}K}},$

wherein V₁, V₂ and CMEV are as determined for said at least one of saidplurality of measurements, and wherein K is a constant.
 12. A method asrecited in claim 9, wherein for at least one of said plurality ofmeasurements said establishing step includes: setting the RMV equal to alowest RMEV corresponding with a prior one of said plurality ofmeasurements.
 13. A method as recited in claim 12, wherein saidestablishing step further includes: computing a motion probabilityfactor (MPF) on an ongoing, updated basis utilizing at least one of saidV₁ values and said V₂ values; and, using the MPF to adjust said RMV whenthe MPF exceeds a predetermined threshold.
 14. A method as recited inclaim 13, wherein said computing step further includes: determining anaverage V₂ value in relation to each given one of a series ofmeasurements by averaging a sum of the V₂ value corresponding with thegiven measurement and the V₂ values corresponding with a predeterminednumber of prior measurements comprising said series, and, comparing theaverage V₂ value corresponding with each given one of the series ofmeasurement periods with the lowest average V₂ value corresponding withany precedent one of said series of measurement periods to obtain aratio therebetween, wherein said ratio is employable as said MPF.
 15. Amethod as recited in claim 14, wherein in said using step said RMV isadjusted to an adjusted RMV as follows: adjustedRMV=CMEV+(CMEV−RMV)(MPF)K, wherein CMEV and MPF are as determined inrelation to a given current measurement and K is a predeterminedconstant.
 16. A method as recited in claim 1, further comprising:determining whether the relative motion estimate value correspondingwith each of said plurality of measurements is within a secondpredetermined range, non-overlapping with said first predeterminedrange, wherein for each measurement having a corresponding relativemotion estimate value within said second predetermined range thecorresponding blood analyte indicator value is employable to obtain atleast one blood analyte concentration value free from adjustment.
 17. Amethod for use in pulse oximetry system which provides a detector outputindicative of light absorption by tissue under test at each a pluralityof different light wavelengths and which employs the detector output tocompute blood analyte indicator value for each of the plurality ofmeasurements, comprising: processing said detector output to obtain aplurality of data sets corresponding with each of said plurality ofmeasurements, each of said plurality of data sets comprising a firstdifferential absorption value for light at a first wavelength and asecond differential absorption value light at a second wave wavelength,wherein for each of said plurality of measurements a corresponding bestfit function is computed for the corresponding plurality of data sets;performing for each of said plurality of measurements a statisticalanalysis of the corresponding plurality of data sets in relation to thecorresponding best fit function to obtain at least one statisticalvariance value; computing a relative motion estimate value for each ofsaid plurality of measurements using said corresponding at least onestatistical variance value; and, determining whether the correspondingrelative motion estimate value for each of said plurality of saidmeasurements is within a first predetermined range, wherein for at leastone of a plurality of measurements having a corresponding relativemotion estimate value within the first predetermined range, thecorresponding blood analyte indicator value is adjusted utilizing apredetermined adjustment factor, and wherein the adjusted blood analyteindicator value is employable to obtain a blood analyte concentrationvalue.
 18. A method as provided in claim 16, wherein said blood analyteindicator values are each defined by a slope value of a regression linecomputed with respect to the corresponding plurality of data sets for agiven measurement, and wherein the predetermined adjustment factor isset to a value between about 0.6 and 0.75.
 19. A method as provided inclaim 17, wherein for each of said plurality of measurements saidperforming step further includes: conducting a principal componentanalysis of the corresponding plurality of data sets in relation to thecorresponding best fit function to obtain at least one of a firstprincipal component variance value (V₁) and a second principal componentvariance value (V₂); and, computing said corresponding relative motionestimate value using at least one of said corresponding first principalcomponent variance value (V₁) and said corresponding second principalcomponent variance value (V₂).
 20. A method as recited in claim 19,wherein said method further includes: establishing a reference motionvalue (RMV); and, wherein for each given one of said plurality ofmeasurements said computing step further includes: calculating acorresponding current motion estimate value (CMEV) using at least one ofsaid corresponding V₁ and V₂ values; and, computing said correspondingrelative motion estimate value (RMEV) using said corresponding CMEV andsaid corresponding RMEV.
 21. A method as recited in claim 20, whereinsaid establishing step further includes: computing a motion probabilityfactor (MPF) on an updated basis utilizing at least one of said V₁values and said V₂ values; and, adjusting said RMV utilizing said MPFwhen said MPF exceeds a predetermined threshold.
 22. A method for use ina pulse oximetry system which provides a detector output indicative oflight absorption by a tissue-under-test at each of a plurality ofdifferent light wavelengths and which employs the detector output tocompute blood analyte indicator values for each of a plurality ofmeasurements, comprising: utilizing said detector output to obtain acorresponding relative motion estimate value for each of said pluralityof measurements by processing said detector output to obtain a differentplurality of data sets corresponding with each of said plurality ofmeasurements, each of said plurality of data sets comprising a firstdifferential absorption value for light at a first wavelength and asecond differential absorption value for light at a second wavelength,wherein for each of said plurality of measurements a corresponding bestfit function is computed for the corresponding plurality of data sets;and, determining whether the corresponding relative motion estimatevalue for each of said plurality of measurements is within a firstpredetermined range, wherein for at least one of said plurality ofmeasurements having a corresponding relative motion estimate valuewithin the first predetermined range the corresponding blood analyteindicator value is adjusted in a predetermined adjustment manner, andwherein said adjusted blood analyte indicator value is employable toobtain a blood analyte concentration value.
 23. A method as cited inclaim 22, wherein for each of said plurality of measurements saidutilizing step further includes: performing a statistical analysis ofthe corresponding plurality of data sets in relation to thecorresponding best fit function to obtain at least one statisticalvariance value indicative of a relative degree of patient motion; and,computing said corresponding relative motion estimate value using saidcorresponding at least one statistical variance value.
 24. A method ascited in claim 22, wherein for each of said plurality of measurementssaid utilizing step further includes: performing a principal componentanalysis of the corresponding plurality of data sets in relation to thecorresponding best fit function to obtain at least one of a firstprincipal component variance value (V₁) and a second principal componentvariance value (V₂); and, computing said corresponding relative motionestimate value using at least one of said corresponding first principalcomponent variance value V₁ and said corresponding second principalcomponent value V₂.
 25. A method as recited in claim 24, wherein foreach given one of said plurality of measurements said utilizing stepfurther includes: calculating a corresponding current motion estimatevalue (CMEV) using at least one of said corresponding V₁ and V₂ values;establishing a reference motion value (RMV); and, computing saidcorresponding relative motion estimate value (RMEV) using saidcorresponding CMEV and said RMV.
 26. A method as recited in claim 25,wherein for each of said plurality of measurements: said CMEV iscalculated as follows: CMEV=V ₁ *V ₂; and, said corresponding RMEV isdetermined as follows: ${RMEV} = {\frac{CMEV}{RMEV}.}$


27. A method as recited in claim 26, wherein for at least one of saidplurality of measurements establishing step includes:${{RMV} = {\frac{V_{1}}{V_{2}}\quad {CMEV}^{*}K}},$

wherein V₁, V₂ and CMEV are as determined for said at least one of saidplurality of measurements, and wherein K is a constant.
 28. A method asrecited in claim 25, wherein for at least one of said plurality ofmeasurements said establishing step includes: setting the RMV equal to alowest RMEV corresponding with a prior one of said plurality ofmeasurements.
 29. A method as recited in claim 27, wherein saidestablishing step further includes: computing a motion probabilityfactor (MPF) on an ongoing, updated basis utilizing at least one of saidV₁ values and said V₂ values; and, using the MPF to adjust said RMV whenthe MPF exceeds a predetermined threshold.
 30. A method as recited inclaim 29, wherein said computing step further includes: determining anaverage V₂ value in relation to each given one of a series ofmeasurements by averaging a sum of the V₂ value corresponding with thegiven measurement and the V₂ values corresponding with a predeterminednumber of prior measurements comprising said series, and, comparing theaverage V₂ value corresponding with each given one of the series ofmeasurement periods with the lowest average V₂ value corresponding withany precedent one of said series of measurement periods to obtain aratio therebetween, wherein said ratio is employable as said MPF.
 31. Amethod as recited in claim 30, wherein in said using step said RMV isadjusted to an adjusted RMV as follows: adjustedRMV=CMEV+(CMEV−RMV)(MPF)K, wherein CMEV and MPF are as determined inrelation to a given current measurement and K is a predeterminedconstant.
 32. A method for use in a pulse oximetry system which providesa detector output indicative of light absorption by a tissue-under-testat each of a plurality of different light wavelengths, comprising:utilizing said detector output to obtain a corresponding relative motionestimate value for each of said plurality of measurements; and,determining: (i.) whether the corresponding relative motion estimatevalue for each of said plurality of measurements is within a firstpredetermined range, wherein for at least one of said plurality ofmeasurements having a corresponding relative motion estimate valuewithin the first predetermined range the corresponding blood analyteindicator value is adjusted in a predetermined manner, and wherein saidadjusted blood analyte indicator value is employable to obtain a bloodanalyte concentration value; and, (ii.) whether the relative motionestimate value corresponding with each of said plurality of measurementsis within a second predetermined range, non-overlapping with said firstpredetermined range, wherein for each measurement having a correspondingrelative motion estimate value within said second predetermined rangethe corresponding blood analyte indicator value is employably to obtainat least one blood analyte concentration value free from adjustment. 33.A method as recited in claim 32, wherein for said at least one of saidplurality of measurements having a corresponding relative motionestimate value within the first predetermined range said predeterminedadjustment manner includes: adjusting the corresponding blood analyteindicator value utilizing a predetermined adjustment factor.
 34. Amethod as recited in claim 33, wherein said adjusting step includes:multiplying the corresponding blood analyte indicator value by saidpredetermined adjustment factor.
 35. A method as recited in claim 34,wherein said adjusting step further includes: comparing thecorresponding blood analyte indicator value to a predetermined thresholdvalue, wherein said predetermined adjustment factor is scaled when saidblood analyte indicator value exceeds said predetermined thresholdvalue.
 36. A method as recited in claim 34, wherein said predeterminedadjustment factor is set to a value between about 0.5 and 0.85.
 37. Amethod as recited in claim 32, said utilizing step including: processingsaid detector output to obtain a different plurality of data setscorresponding with each of said plurality of measurements, each of saidplurality of data sets comprising a first differential absorption valuefor light at a first wavelength and a second differential absorptionvalue for light at a second wavelength, wherein for each of saidplurality of measurements a corresponding best fit function is computedfor the corresponding plurality of data sets.
 38. A method as cited inclaim 37, wherein for each of said plurality of measurements saidutilizing step further includes: performing a statistical analysis ofthe corresponding plurality of data sets in relation to thecorresponding best fit function to obtain at least one statisticalvariance value indicative of a relative degree of patient motion; and,computing said corresponding relative motion estimate value using saidcorresponding at least one statistical variance value.
 39. A method foruse in a pulse oximetry system which provides a detector outputindicative of light absorption by a tissue-under-test at each of aplurality of different light wavelengths, comprising: utilizing saiddetector output to (i.) compute blood analyte indicator values for eachof a plurality of measurements, and (ii.) obtain a correspondingrelative motion estimate value for each of said plurality ofmeasurements; and, determining whether the corresponding relative motionestimate value for each of said plurality of measurements is within afirst predetermined range, wherein for at least one of said plurality ofmeasurements having a corresponding relative motion estimate valuewithin the first predetermined range the corresponding blood analyteindicator value is adjusted in a predetermined adjustment manner; and,separately employing, for each of said plurality of measurements, thecorresponding blood analyte indicator value, as adjusted, to obtain ablood analyte concentration value.
 40. A method as recited in claim 39,wherein for said at least one of said plurality of measurements having acorresponding relative motion estimate value within the firstpredetermined range said predetermined adjustment manner includes:adjusting the corresponding blood analyte indicator value utilizing apredetermined adjustment factor.
 41. A method as recited in claim 40,wherein said adjusting step includes: multiplying the correspondingblood analyte indicator value by said predetermined adjustment factor.42. A method as recited in claim 41, wherein said adjusting step furtherincludes: comparing the corresponding blood analyte indicator value to apredetermined threshold value, wherein said predetermined adjustmentfactor is scaled when said blood analyte indicator value exceeds saidpredetermined threshold value.
 43. A method as recited in claim 41,wherein said predetermined adjustment factor is set to a value betweenabout 0.5 and 0.85.
 44. A method as recited in claim 39, said utilizingstep including: processing said detector output to obtain a differentplurality of data sets corresponding with each of said plurality ofmeasurements, each of said plurality of data sets comprising a firstdifferential absorption value for light at a first wavelength and asecond differential absorption value for light at a second wavelength,wherein for each of said plurality of measurements a corresponding bestfit function is computed for the corresponding plurality of data sets.45. A method as cited in claim 44, wherein for each of said plurality ofmeasurements said utilizing step further includes: performing astatistical analysis of the corresponding plurality of data sets inrelation to the corresponding best fit function to obtain at least onestatistical variance value indicative of a relative degree of patientmotion; and, computing said corresponding relative motion estimate valueusing said corresponding at least one statistical variance value.